Introduction to Data Warehousing Introduction to Data Warehousing Ms Swapnil Shrivastava [email protected]
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May 29, 2020
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Introduction to Data WarehousingIntroduction to Data Warehousing
Ms Swapnil Shrivastava
Why Data Warehouse?Why Data Warehouse?
Necessity is the mother of invention
Scenario 1Scenario 1
ABC Pvt Ltd is a company with branches at Mumbai, Delhi, Chennai and Banglore. The Sales Manager wants quarterly sales report. Each branch has a separate operational system.
Scenario 1 : ABC Pvt Ltd.Scenario 1 : ABC Pvt Ltd.
Mumbai
Delhi
Chennai
Banglore
SalesManager
Sales per item type per branchfor first quarter.
Solution 1:ABC Pvt Ltd.Solution 1:ABC Pvt Ltd.
Extract sales information from each database. Store the information in a common repository at a
single site.
Solution 1:ABC Pvt Ltd.Solution 1:ABC Pvt Ltd.
Mumbai
Delhi
Chennai
Banglore
DataWarehouse
SalesManager
Query &Analysis tools
Report
Scenario 2Scenario 2
One Stop Shopping Super Market has hugeoperational database.Whenever Executives wantssome report the OLTP system becomesslow and data entry operators have to wait for some time.
Scenario 2 : One Stop ShoppingScenario 2 : One Stop Shopping
OperationalDatabase
Data Entry Operator
Data Entry Operator
ManagementWait
Report
Solution 2Solution 2
Extract data needed for analysis from operational database.
Store it in warehouse. Refresh warehouse at regular interval so that it
contains up to date information for analysis. Warehouse will contain data with historical
perspective.
Solution 2Solution 2
Operationaldatabase
DataWarehouse
Extractdata
Data EntryOperator
Data EntryOperator
Manager
Report
Transaction
Scenario 3Scenario 3
Cakes & Cookies is a small,new company.President of the company wants his company should grow.He needs information so that he can make correct decisions.
Solution 3Solution 3
Improve the quality of data before loading it into the warehouse.
Perform data cleaning and transformation before loading the data.
Use query analysis tools to support adhoc queries.
Solution 3Solution 3
Query and Analysistool
President
Expansion
Improvement
sales
time
DataWarehouse
What is Data Warehouse??What is Data Warehouse??
Inmons’s definitionInmons’s definition
A data warehouse is-subject-oriented,-integrated,-time-variant,-nonvolatile
collection of data in support of management’sdecision making process.
Subject-orientedSubject-oriented
Data warehouse is organized around subjects such as sales,product,customer.
It focuses on modeling and analysis of data for decision makers.
Excludes data not useful in decision support process.
IntegrationIntegration
Data Warehouse is constructed by integrating multiple heterogeneous sources.
Data Preprocessing are applied to ensure consistency.
RDBMS
LegacySystem
DataWarehouse
Flat File Data ProcessingData Transformation
IntegrationIntegration In terms of data.
– encoding structures.
– Measurement ofattributes.
– physical attribute. of data
– naming conventions.
– Data type format
remarks
Time-variantTime-variant
Provides information from historical perspective e.g. past 5-10 years
Every key structure contains either implicitly or explicitly an element of time
NonvolatileNonvolatile
Data once recorded cannot be updated. Data warehouse requires two operations in data
accessing– Initial loading of data– Access of data
load
access
Operational v/s Information SystemOperational v/s Information System
Complex queryShort ,simple transactionUnit of work
Summarized, multidimensional
Detailed,flat relationalView
Subject orientedApplication orientedDB design
Mostly readRead/writeAccess
HistoricalCurrentData
Decision supportDay to day operationFunction
Knowledge workersClerk,DBA,database professional
User
AnalysisTransactionOrientation
Informational processingOperational processingCharacteristics
InformationOperationalFeatures
Operational v/s Information SystemOperational v/s Information System
High flexibility,end-user autonomy
High performance,high availability
Priority
Query througputTransaction throughputMetric
InformationOperationalFeatures
100 GB to TB100MB to GBDB size
hundredsthousandsNumber of users
millionstensNumber of records accessed
Information outData inFocus
Data Warehousing ArchitectureData Warehousing Architecture
Extract Transform
Load Refresh
Serve
ExternalSources
Operational Dbs
Analysis
Query/Reporting
Data Mining
Monitoring &Administration
MetadataRepository
DATA SOURCES TOOLS
DATA MARTS
OLAP Servers
Reconciled data
Data Warehouse ArchitectureData Warehouse Architecture
Data Warehouse server– almost always a relational DBMS,rarely flat
files OLAP servers
– to support and operate on multi-dimensional data structures
Clients– Query and reporting tools– Analysis tools– Data mining tools
Data Warehouse SchemaData Warehouse Schema
Star Schema Fact Constellation Schema Snowflake Schema
Star SchemaStar Schema
A single,large and central fact table and one table for each dimension.
Every fact points to one tuple in each of the dimensions and has additional attributes.
Does not capture hierarchies directly.
Star Schema (contd..)Star Schema (contd..)
Price
Units
Period Key
Product Key
Store Key
Store Dimension Time Dimension
Product Dimension
Fact Table
Benefits: Easy to understand, easy to define hierarchies, reduces no. of physical joins.
Store Key
Region
State
City
Store Name
Month
Quarter
Year
Period Key
Product Desc
Product Key
SnowFlake SchemaSnowFlake Schema
Variant of star schema model. A single,large and central fact table and one
or more tables for each dimension. Dimension tables are normalized i.e. split
dimension table data into additional tables
SnowFlake Schema (contd..)SnowFlake Schema (contd..)
Price
Units
Period Key
Product Key
Store Key
Time Dimension
Product Dimension
Fact Table
Store Key
City Key
Store Name
Month
Quarter
Year
Period Key
Product Desc
Product Key
State
City Key
Region
City
City Dimension
Store Dimension
Drawbacks: Time consuming joins,report generation slow
Fact ConstellationFact Constellation
Multiple fact tables share dimension tables. This schema is viewed as collection of stars
hence called galaxy schema or fact constellation.
Sophisticated application requires such schema.
Fact Constellation (contd..)Fact Constellation (contd..)
Price
Units
Period Key
Product Key
Store Key
Store Dimension
Product Dimension
SalesFact Table
Store Key
Region
State
City
Store Name
Product Desc
Product KeyShipper Key
Price
Units
Period Key
Product Key
Store Key
ShippingFact Table
Building Data WarehouseBuilding Data Warehouse
Data Selection Data Preprocessing
– Fill missing values– Remove inconsistency
Data Transformation & Integration Data Loading Data in warehouse is stored in form of fact tables
and dimension tables.
Case StudyCase Study Afco Foods & Beverages is a new company which
produces dairy,bread and meat products with production unit located at Baroda.
There products are sold in North,North West and Western region of India.
They have sales units at Mumbai, Pune , Ahemdabad ,Delhi and Baroda.
The President of the company wants sales information.
Sales InformationSales Information
Report: The number of units sold.
113
Report: The number of units sold over time
25334114
AprilMarchFebruaryJanuary
Sales InformationSales InformationReport : The number of items sold for each product withtime
21258Swiss Rolls
86166Cheese
176Wheat Bread
AprMarFebJan
Product
Tim
e
Sales InformationSales InformationReport: The number of items sold in each City for each product with time
1594Swiss Rolls
83Cheese
73Wheat BreadPune
6164Swiss Rolls
6163Cheese
103Wheat BreadMumbai
AprMarFebJan
Product
Tim
e
City
Sales InformationSales Information
Report: The number of items sold and income in each region for each product with time.
AprMarFebJan
21.20
17.36
24.80
Rs
27.45
7.44
10.98
15.90
7.44
Rs
16.47
29.98
42.40
Rs
7.32
7.95
7.32
7.95
Rs
1594Swiss Rolls
83Cheese
73Wheat BreadPune
6164Swiss Rolls
6163Cheese
103Wheat BreadMumbai
UUUU
Sales Measures & DimensionsSales Measures & Dimensions
Measure – Units sold, Amount. Dimensions – Product,Time,Region.
Sales Data Warehouse ModelSales Data Warehouse Model
42.4016FebruarySwiss RollsMumbai
7.324JanuaryCheesePune
7.953JanuaryWheat BreadPune
7.324JanuaryCheeseMumbai
7.953JanuaryWheat BreadMumbai
RupeesUnitsMonthProductCity
Fact Table
Sales Data Warehouse ModelSales Data Warehouse Model
42.40162/1/19985901
7.3241/1/199812182
7.9531/1/19985892
7.3241/1/199812181
7.9531/1/19985891
RupeesUnitsMonthProd_IDCity_ID
Sales Data Warehouse ModelSales Data Warehouse Model
Product Dimension Tables
2Coconut Cookies288
1Swiss Rolls590
1Wheat Bread589
Product_Category_IDProduct_NameProd_ID
Cookies2
Bread1
Product_CategoryProduct_Category_Id
Sales Data Warehouse ModelSales Data Warehouse Model
Region Dimension Table
IndiaNorthWestPune2
IndiaWestMumbai1
CountryRegionCityCity_ID
Sales Data Warehouse ModelSales Data Warehouse Model
Sales Fact
Region
ProductProduct
Category
Time
Online Analysis Processing(OLAP)Online Analysis Processing(OLAP)
It enables analysts, managers and executives to gain insight into data through fast, consistent, interactive access to a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as understood by the user.
Data Warehouse
Time
Product
Reg
ion
OLAP CubeOLAP Cube
7.443MarchWheat BreadMumbai
7.443Qtr1Wheat BreadMumbai
32.2413AllWheat BreadMumbai
98.4938AllWhite BreadMumbai
146.0764AllAllMumbai
251.26113AllAllAll
DollarsUnitsTimeProductCity
OLAP OperationsOLAP OperationsDrill Down
Time
Reg
ion
Product
Category e.g Electrical Appliance
Sub Category e.g Kitchen
Product e.g Toaster
OLAP OperationsOLAP OperationsDrill Up
Time
Reg
ion
Product
Category e.g Electrical Appliance
Sub Category e.g Kitchen
Product e.g Toaster
OLAP OperationsOLAP OperationsSlice and Dice
Time
Reg
ion
ProductProduct=Toaster
Time
Reg
ion
OLAP OperationsOLAP OperationsPivot
Time
Reg
ion
Product
RegionT
ime
Product
OLAP ServerOLAP Server
An OLAP Server is a high capacity,multi user data manipulation engine specifically designed to support and operate on multi-dimensional data structure.
OLAP server available are– MOLAP server– ROLAP server– HOLAP server
PresentationPresentation
Time
Reg
ion
Product
Report
ReportingTool
Data Warehousing includesData Warehousing includes
Build Data Warehouse Online analysis processing(OLAP). Presentation.
RDBMS
Flat File
Presentation
Cleaning ,Selection &Integration
Warehouse & OLAP serverClient
Need for Data WarehousingNeed for Data Warehousing
Industry has huge amount of operational data Knowledge worker wants to turn this data into
useful information. This information is used by them to support
strategic decision making .
Need for Data Warehousing (contd..)Need for Data Warehousing (contd..)
It is a platform for consolidated historical data for analysis.
It stores data of good quality so that knowledge worker can make correct decisions.
Need for Data Warehousing (contd..)Need for Data Warehousing (contd..)
From business perspective
-it is latest marketing weapon
-helps to keep customers by learning more about their needs .
-valuable tool in today’s competitive fast evolving world.
Data Warehousing ToolsData Warehousing Tools
Data Warehouse– SQL Server 2000 DTS– Oracle 8i Warehouse Builder
OLAP tools– SQL Server Analysis Services– Oracle Express Server
Reporting tools– MS Excel Pivot Chart– VB Applications
ReferencesReferences
Building Data Warehouse by Inmon Data Mining:Concepts and Techniques by Han,Kamber. www.dwinfocenter.org www.datawarehousingonline.com www.billinmon.com
Thank YouThank You
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